The following description provides a summary of information relevant to the present application and is not an admission that any of the information provided or publications referenced herein is prior art to the present application.
More people die from lung cancer than any other type of cancer. This is true for both men and women. In 2005 in the United States (the most recent year for which statistics are currently available), lung cancer accounted for more deaths than breast cancer, prostate cancer, and colon cancer combined. In that year, 107,416 men and 89,271 women were diagnosed with lung cancer, and 90,139 men and 69,078 women died from lung cancer. Among men in the United States, lung cancer is the second most common cancer among white, black, Asian/Pacific Islander, American Indian/Alaska Native, and Hispanic men. Among women in the United States, lung cancer is the second most common cancer among white, black, and American Indian/Alaska Native women, and the third most common cancer among Asian/Pacific Islander and Hispanic women. For those who do not quit smoking, the probability of death from lung cancer is 15% and remains above 5% even for those who quit at age 50-59. The annual healthcare cost of lung cancer in the U.S. alone is $95 billion.
Ninety-one percent of lung cancer caused by smoking is non-small cell lung cancer (NSCLC), which represents approximately 87% of all lung cancers. The remaining 13% of all lung cancers are small cell lung cancers, although mixed-cell lung cancers do occur. Because small cell lung cancer is rare and rapidly fatal, the opportunity for early detection is small.
There are three main types of NSCLC: squamous cell carcinoma, large cell carcinoma, and adenocarcinoma. Adenocarcinoma is the most common form of lung cancer (30%-40% and reported to be as high as 50%) and is the lung cancer most frequently found in both smokers and non-smokers. Squamous cell carcinoma accounts for 25-30% of all lung cancers and is generally found in a proximal bronchus. Early stage NSCLC tends to be localized, and if detected early it can often be treated by surgery with a favorable outcome and improved survival. Other treatment options include radiation treatment, drug therapy, and a combination of these methods.
NSCLC is staged by the size of the tumor and its presence in other tissues including lymph nodes. In the occult stage, cancer cells are found in sputum samples or lavage samples and no tumor is detectable in the lungs. In stage 0, only the innermost lining of the lungs exhibit cancer cells and the tumor has not grown through the lining. In stage IA, the cancer is considered invasive and has grown deep into the lung tissue but the tumor is less than 3 cm across. In this stage, the tumor is not found in the bronchus or lymph nodes. In stage IB, the tumor is either larger than 3 cm across or has grown into the bronchus or pleura, but has not grown into the lymph nodes. In stage IIA, the tumor is more than 3 cm across and has grown into the lymph nodes. In stage IIB, the tumor has either been found in the lymph nodes and is greater than 3 cm across or grown into the bronchus or pleura; or the cancer is not in the lymph nodes but is found in the chest wall, diaphragm, pleura, bronchus, or tissue that surrounds the heart. In stage IIIA, cancer cells are found in the lymph nodes near the lung and bronchi and in those between the lungs but on the side of the chest where the tumor is located. Stage IIIB, cancer cells are located on the opposite side of the chest from the tumor and in the neck. Other organs near the lungs may also have cancer cells and multiple tumors may be found in one lobe of the lungs. In stage IV, tumors are found in more than one lobe of the same lung or both lungs and cancer cells are found in other parts of the body.
Current methods of diagnosis for lung cancer include testing sputum for cancerous cells, chest x-ray, fiber optic evaluation of airways, and low dose spiral computed tomography (CT). Sputum cytology has a very low sensitivity. Chest X-ray is also relatively insensitive, requiring lesions to be greater than 1 cm in size to be visible. Bronchoscopy requires that the tumor is visible inside airways accessible to the bronchoscope. The most widely recognized diagnostic method is CT, but in common with X-ray, the use of CT involves ionizing radiation, which itself can cause cancer. CT also has significant limitations: the scans require a high level of technical skill to interpret and many of the observed abnormalities are not in fact lung cancer and substantial healthcare costs are incurred in following up CT findings. The most common incidental finding is a benign lung nodule.
Lung nodules are relatively round lesions, or areas of abnormal tissue, located within the lung and may vary in size. Lung nodules may be benign or cancerous, but most are benign. If a nodule is below 4 mm the prevalence is only 1.5%, if 4-8 mm the prevalence is approximately 6%, and if above 20 mm the incidence is approximately 20%. For small and medium-sized nodules, the patient is advised to undergo a repeat scan within three months to a year. For many large nodules, the patient receives a biopsy (which is invasive and may lead to complications) even though most of these are benign.
Therefore, diagnostic methods that can replace or complement CT are needed to reduce the number of surgical procedures conducted and minimize the risk of surgical complications. In addition, even when lung nodules are absent or unknown, methods are needed to detect lung cancer at its early stages to improve patient outcomes. Only 16% of lung cancer cases are diagnosed as localized, early stage cancer, where the 5-year survival rate is 46%, compared to 84% of those diagnosed at late stage, where the 5-year survival rate is only 13%. This demonstrates that relying on symptoms for diagnosis is not useful because many of them are common to other lung disease. These symptoms include a persistent cough, bloody sputum, chest pain, and recurring bronchitis or pneumonia.
Where methods of early diagnosis in cancer exist, the benefits are generally accepted by the medical community. Cancers that have widely utilized screening protocols have the highest 5-year survival rates, such as breast cancer (88%) and colon cancer (65%) versus 16% for lung cancer. However, 88% of lung cancer patients survive ten years or longer if the cancer is diagnosed at Stage 1 through screening. This demonstrates the clear need for diagnostic methods that can reliably detect early-stage NSCLC.
Biomarker selection for a specific disease state involves first the identification of markers that have a measurable and statistically significant difference in a disease population compared to a control population for a specific medical application. Biomarkers can include secreted or shed molecules that parallel disease development or progression and readily diffuse into the blood stream from lung tissue or from distal tissues in response to a lesion. The biomarker or set of biomarkers identified are generally clinically validated or shown to be a reliable indicator for the original intended use for which it was selected. Biomarkers can include small molecules, peptides, proteins, and nucleic acids. Some of the key issues that affect the identification of biomarkers include over-fitting of the available data and bias in the data.
A variety of methods have been utilized in an attempt to identify biomarkers and diagnose disease. For protein-based markers, these include two-dimensional electrophoresis, mass spectrometry, and immunoassay methods. For nucleic acid markers, these include mRNA expression profiles, microRNA profiles, FISH, serial analysis of gene expression (SAGE), and large scale gene expression arrays.
The utility of two-dimensional electrophoresis is limited by low detection sensitivity; issues with protein solubility, charge, and hydrophobicity; gel reproducibility; and the possibility of a single spot representing multiple proteins. For mass spectrometry, depending on the format used, limitations revolve around the sample processing and separation, sensitivity to low abundance proteins, signal to noise considerations, and inability to immediately identify the detected protein. Limitations in immunoassay approaches to biomarker discovery are centered on the inability of antibody-based multiplex assays to measure a large number of analytes. One might simply print an array of high-quality antibodies and, without sandwiches, measure the analytes bound to those antibodies. (This would be the formal equivalent of using a whole genome of nucleic acid sequences to measure by hybridization all DNA or RNA sequences in an organism or a cell. The hybridization experiment works because hybridization can be a stringent test for identity. Even very good antibodies are not stringent enough in selecting their binding partners to work in the context of blood or even cell extracts because the protein ensemble in those matrices have extremely different abundances.) Thus, one must use a different approach with immunoassay-based approaches to biomarker discovery—one would need to use multiplexed ELISA assays (that is, sandwiches) to get sufficient stringency to measure many analytes simultaneously to decide which analytes are indeed biomarkers. Sandwich immunoassays do not scale to high content, and thus biomarker discovery using stringent sandwich immunoassays is not possible using standard array formats. Lastly, antibody reagents are subject to substantial lot variability and reagent instability. The instant platform for protein biomarker discovery overcomes this problem.
Many of these methods rely on or require some type of sample fractionation prior to the analysis. Thus the sample preparation required to run a sufficiently powered study designed to identify/discover statistically relevant biomarkers in a series of well-defined sample populations is extremely difficult, costly, and time consuming During fractionation, a wide range of variability can be introduced into the various samples. For example, a potential marker could be unstable to the process, the concentration of the marker could be changed, inappropriate aggregation or disaggregation could occur, and inadvertent sample contamination could occur and thus obscure the subtle changes anticipated in early disease.
It is widely accepted that biomarker discovery and detection methods using these technologies have serious limitations for the identification of diagnostic biomarkers. These limitations include an inability to detect low-abundance biomarkers, an inability to consistently cover the entire dynamic range of the proteome, irreproducibility in sample processing and fractionation, and overall irreproducibility and lack of robustness of the method. Further, these studies have introduced biases into the data and not adequately addressed the complexity of the sample populations, including appropriate controls, in terms of the distribution and randomization required to identify and validate biomarkers within a target disease population.
Although efforts aimed at the discovery of new and effective biomarkers have gone on for several decades, the efforts have been largely unsuccessful. Biomarkers for various diseases typically have been identified in academic laboratories, usually through an accidental discovery while doing basic research on some disease process. Based on the discovery and with small amounts of clinical data, papers were published that suggested the identification of a new biomarker. Most of these proposed biomarkers, however, have not been confirmed as real or useful biomarkers, primarily because the small number of clinical samples tested provide only weak statistical proof that an effective biomarker has in fact been found. That is, the initial identification was not rigorous with respect to the basic elements of statistics. In each of the years 1994 through 2003, a search of the scientific literature shows that thousands of references directed to biomarkers were published. During that same time frame, however, the FDA approved for diagnostic use, at most, three new protein biomarkers a year, and in several years no new protein biomarkers were approved.
Based on the history of failed biomarker discovery efforts, mathematical theories have been proposed that further promote the general understanding that biomarkers for disease are rare and difficult to find. Biomarker research based on 2D gels or mass spectrometry supports these notions. Very few useful biomarkers have been identified through these approaches. However, it is usually overlooked that 2D gel and mass spectrometry measure proteins that are present in blood at approximately 1 nM concentrations and higher, and that this ensemble of proteins may well be the least likely to change with disease. Other than the instant biomarker discovery platform, proteomic biomarker discovery platforms that are able to accurately measure protein expression levels at much lower concentrations do not exist.
Much is known about biochemical pathways for complex human biology. Many biochemical pathways culminate in or are started by secreted proteins that work locally within the pathology, for example growth factors are secreted to stimulate the replication of other cells in the pathology, and other factors are secreted to ward off the immune system, and so on. While many of these secreted proteins work in a paracrine fashion, some operate distally in the body. One skilled in the art with a basic understanding of biochemical pathways would understand that many pathology-specific proteins ought to exist in blood at concentrations below (even far below) the detection limits of 2D gels and mass spectrometry. What must precede the identification of this relatively abundant number of disease biomarkers is a proteomic platform that can analyze proteins at concentrations below those detectable by 2D gels or mass spectrometry.
Accordingly, a need exists for biomarkers, methods, devices, reagents, systems, and kits that enable (a) the differentiation of benign pulmonary nodules from malignant pulmonary nodules; (b) the detection of lung cancer biomarkers; and (c) the diagnosis of lung cancer.